Unit Synopsis
Artificial Intelligence (AI) is transforming the way we interact with technology, enabling machines to think, learn, and adapt in ways that mimic human intelligence. From intelligent chatbots to autonomous robotics, AI is becoming an essential part of our everyday lives and has the potential to transform entire industries. This unit introduces the core concepts of AI, starting with foundational principles and real-world applications. You will explore key machine learning approaches, including both supervised and unsupervised learning, and examine advanced topics such as reinforcement learning, classical and heuristic search strategies, and deep learning, with a focus on convolutional and recurrent neural networks for tasks like image classification and natural language processing. Additionally, you will examine ethical AI practices, addressing the societal impact of AI and the importance of ensuring fairness, transparency, and accountability in AI systems. The unit also covers cutting-edge trends like cloud-based AI and AI at the edge, which are shaping the future of AI deployment. Through programming and problem-based assessments, you will gain both theoretical knowledge and practical skills in modern AI technologies.
Details
| Level | Postgraduate |
|---|---|
| Unit Level | 9 |
| Credit Points | 6 |
| Student Contribution Band | SCA Band 2 |
| Fraction of Full-Time Student Load | 0.125 |
| Pre-requisites or Co-requisites |
Pre-requisite: COIT20245 Introduction to Programming
Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework). |
| Class Timetable | View Unit Timetable |
| Residential School | No Residential School |
Unit Availabilities from Term 2 - 2027
Term 2 - 2027 Profile
Attendance Requirements
All on-campus students are expected to attend scheduled classes - in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).
Recommended Student Time Commitment
Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.
Assessment Tasks
To see assessment details from an earlier availability, please search via a previous term.
This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%).
Past Exams
All University policies are available on the Policy web site, however you may wish to directly view the following policies below.
This list is not an exhaustive list of all University policies. The full list of policies are available on the Policy web site.
Feedback, Recommendations and Responses
Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.
Source: Student feedback
Too much material and too little time.
This unit is designed as an introduction to AI, providing a broad overview of major subareas to prepare students for advanced units such as COIT29224 Evolutionary Computation and COIT29225 Neural Networks and Deep Learning. In response to feedback, the two weekly topics on Cloud AI and Edge AI will be combined into one week, allowing for extended coverage of Deep Learning with an additional week.
In 2026 Term 1, the two weekly topics on Cloud AI and Edge AI were combined into a single week, increasing Deep Learning coverage by an additional week to better prepare students for advanced units like COIT29224 and COIT29225.
Source: Student feedback
Students reported improved understanding of AI, with weekly quizzes suggested as a useful addition.
A summative quiz could be introduced in tutorials following major topic areas, such as Machine Learning or Search Techniques (typically spanning two to three weeks), to reinforce key concepts, provide timely feedback, and support ongoing student engagement.
This recommendation remains under discussion, as summative quizzes present GenAI academic integrity risks and offer limited diagnostic value for postgraduate-level assessment compared to practical evaluations.
Source: UC Reflection
Assessment feedback should more consistently explain the marks awarded and provide clear, actionable guidance for achieving higher performance levels.
Provide consistent, criterion-specific feedback that clearly explains the marks awarded and identifies actionable steps for improvement.
In Progress
Source: UC Reflection
Greater use of interactive, real-time problem-solving and practical technical demonstrations would enhance student engagement and learning.
Increase the use of live demonstrations and practical problem-solving activities to promote active learning.
In Progress
To see Learning Outcomes from an earlier availability, please search via a previous term.